Foreground extraction via dual-side cameras on a mobile device using long short-term trajectory analysis

摘要

This paper presents a foreground extraction method for live-streaming videos using dual-side cameras on mobile devices. Compared to conventional methods, which estimate both foreground and background models from the front camera, the proposed method uses the rear camera to infer the reference background model. To this end, the short-term trajectory analysis is first performed to cluster point trajectories of the front camera, and then the long-term trajectory analysis is performed to compare the paths of the clustered trajectories with the reference path obtained from the rear camera. In particular, clusters having high correlation are classified as background using the Gaussian mixture model. Additionally, a pixel-wise segmentation map is obtained via graph-based segmentation. Experimental results show that the proposed method is robust under a variety of camera motion, outperforming state-of-the-art methods. Code and dataset can be found at https://github.com/YCL92/dualCamSeg. (C) 2019 Elsevier B.V. All rights reserved.